DESCRIPTION (APPLICATION ABSTRACT): The aims of this proposed two-year descriptive correlational study build on the established integrity and capacity of the California Nursing Outcomes Coalition (CalNOC) to engage California acute care hospitals in voluntarily using ANA nursing quality indicators for reporting standardized nurse staffing, patient safety, and quality indicators in a collaborative research, repository development, and benchmarking project. For the purposes of this study, it is posited that the daily unit-level configuration of nurse staffing and workload may buffer patients from the effects of error and resulting injury or compromise patient safety when variance in these factors exceeds a staff's adaptive capacity and breaches a unit-level margin of safety. The aims of this study are grounded in the knowledge that the potential to compromise patient safety through human error is inherent in nursing practice and medical care (IOM, 1999; QUIC, 2000; Reason, 1990). In collaboration with CalNOC's Statewide voluntary-convenience sample of medical-surgical acute care units from 77 hospitals, this study will break new ground in tracing daily unit-level direct care nurse staffing, in 100 patient care units over a two-month period, to examine associations between the structure of hospital nurse staffing and authoritative indicators of patient outcomes and safety commonly tracked by acute care hospitals, as well as regulatory and accreditation agencies--falls, pressure ulcers, restraint prevalence, and significant clinical events. Staffing measures to be studied include hours of direct care per patient day, skill mix of nurse caregivers, percent of contacted or agency staff, ratio of required to actual hours of care, and RN years of post-licensure experience. This study recognizes and quantifies the impact of patient turnover, a key factor in nurse staffing workload, and integrates it into multiple regression analyses examining associations between nurse staffing and outcomes. Of equal importance, this study will advance staffing measurement by tracing and analyzing the impact of variation in staffing and patient turnover, exploring the impact of variance on patient safety and outcomes.